1. Field of the Invention
The present invention relates to an object identification apparatus which identifies a class to which input data belongs, and a control method thereof.
2. Description of the Related Art
Many techniques for identifying a class which is registered in advance and to which an object in input data belongs by comparing the input data with data which are registered in advance have been proposed. As an application field of these techniques, a face identification technique that identifies an individual face is known. A class in face identification includes a name or ID which can be used to identify an individual, and a registered image is appended in advance with an identifier of that class. Assume that even under various shooting conditions of registered images, registered images including images of an identical person belong to an identical class. In the following description of the present specification, identification of objects means determination of individual differences of objects (for example, personal differences). On the other hand, detection of objects means determination of objects which belong to an identical category without discriminating individuals (for example, detection of faces without discriminating individuals).
As the face identification technique, for example, a method of identifying a face using partial feature amounts of an image is described in Japanese Patent Laid-Open No. 2003-323622 (to be referred to as patent reference 1 hereinafter). In the method described in patent reference 1, partial feature amounts are extracted from some characteristic partial areas in an input image, and partial feature amounts are also extracted from partial areas corresponding to those of the input image in each registered image at the same time. Then, similarities obtained by comparing corresponding feature amounts between the input image and each registered image are integrated to obtain a similarity between the input image and that registered image. Finally, a class to which a registered image having the largest similarity with this input image belongs is determined as that corresponding to the input image.
The aforementioned partial areas required to calculate partial feature amounts can be calculated in advance by a method such as the AdaBoost method used in, for example, [Shen, L., Bai, L., Bardsley, D., Wang, Y., Gabor feature selection for face recognition using improved adaboost learning. Proceedings of International Workshop on Biometric Recognition System, in conjunction with ICCV'05, 2005] (to be referred as non-patent reference 1 hereinafter). On the other hand, for this prior learning, a sequential learning method for improving the recognition performance while sequentially adding supervisory data during operation of recognition processing, as described in [Glenn Fung, O. L. Mangasarian, Incremental support vector machine classification, Proceedings of Second SIAM International Conference on Data Mining, 2002.] (to be referred to as non-patent reference 2 hereinafter) is popularly used not only in image processing but also in a variety of fields.
In the face identification method using partial areas, typically, the positions and number of partial areas are determined in advance so as to enhance average identification performance for every identification targets. However, for example, when only targets which belong to a few specific classes are to be accurately identified, the average identification performance does not suffice. Also, the sequential learning method described in non-patent reference 2 aims at improving identification performance for specific targets by sequentially adding data of targets for which the identification performance is to be improved as supervisory data. However, an identification unit acquired by sequential learning tends to be excessively optimized to data given as supervisory data. For this reason, by repeating the sequential learning, the identification performance for targets other than the specific targets is extremely worsened, that is, over-learning occurs.